Domain-Specific Customization for Language Models in Otolaryngology: The ENT GPT Assistant

Objective: To develop and evaluate the effectiveness of domain-specific customization in large language models (LLMs) by assessing the performance of the ENT GPT Assistant (E-GPT-A), a model specifically tailored for otolaryngology. Study Design: Comparative analysis using multiple-choice questions (MCQs) from established otolaryngology resources. Setting: Tertiary care academic hospital. Methods: Two hundred forty clinical-vignette style MCQs were sourced from BoardVitals Otolaryngology and OTOQuest, covering a range of otolaryngology subspecialties (n = 40 for each). The E-GPT-A was developed using targeted instructions and customized to otolaryngology. The performance of E-GPT-A was compared against top-performing and widely used artificial intelligence (AI) LLMs, including GPT-3.5, GPT-4, Claude 2.0, and Claude 2.1. Accuracy was assessed across subspecialties, varying question difficulty tiers, and in diagnostics and management. Results: E-GPT-A achieved an overall accuracy of 74.6%, outperforming GPT-3.5 (60.4%), Claude 2.0 (61.7%), Claude 2.1 (60.8%), and GPT-4 (68.3%). The model performed best in allergy and rhinology (85.0%) and laryngology (82.5%), whereas showing lower accuracy in pediatrics (62.5%) and facial plastics/reconstructive surgery (67.5%). Accuracy also declined as question difficulty increased. The average correct response percentage among otolaryngologists and otolaryngology trainees was 71.1% in the question set. Conclusion: This pilot study using the E-GPT-A demonstrates the potential benefits of domain-specific customizations of language models for otolaryngology. However, further development, continuous updates, and continued real-world validation are needed to fully assess the capabilities of LLMs in otolaryngology.